We compare the results against traditional variational autoencoders and generative adversarial networks. The results show reconstruction of the inputs with feature and generator. We also show random samples from the generator network. We evaluate the Fréchet Inception Distance (FID) [1]}, [2]} for each data set as a measure of the quality of the samples. The FID score is calculated by extracting the activation of the global spatial pooling layer of a pre-trained Inception V3 model [3]}, for equally numbered images from the data set (here we choose 10k images) and sampled from a generator model:
\(FID = ||\mu _1 - \mu _2||_2^2 + tr(\Sigma _1 + \Sigma _2 - 2\sqrt{\Sigma _1 \Sigma _2}),\) 
